arXiv subjects

Ge Wang

Publications and source records attributed to Ge Wang.

At least 73 records · Page 4Linked to original sources

arXiv2025

N-ReLU: Zero-Mean Stochastic Extension of ReLU

Activation functions are fundamental for enabling nonlinear representations in deep neural networks. However, the standard rectified linear unit (ReLU) often suffers from inactive or "dead" neurons caused by its hard zero cutoff. To address this issue, we introduce N-ReLU (Noise-ReLU), a zero-mean stochastic extension of ReLU that replaces negative activations with Gaussian noise while preserving the same expected output. This expectation-aligned formulation maintains gradient flow in inactive regions and acts as an annealing-style regularizer during training. Experiments on the MNIST dataset using both multilayer perceptron (MLP) and convolutional neural network (CNN) architectures show that N-ReLU achieves accuracy comparable to or slightly exceeding that of ReLU, LeakyReLU, PReLU, GELU, and RReLU at moderate noise levels (sigma = 0.05-0.10), with stable convergence and no dead neurons observed. These results demonstrate that lightweight Gaussian noise injection offers a simple yet effective mechanism to enhance optimization robustness without modifying network structures or introducing additional parameters.

cs.LG↗

arXiv2025

We Can Hear You with mmWave Radar! An End-to-End Eavesdropping System

With the rise of voice-enabled technologies, loudspeaker playback has become widespread, posing increasing risks to speech privacy. Traditional eavesdropping methods often require invasive access or line-of-sight, limiting their practicality. In this paper, we present mmSpeech, an end-to-end mmWave-based eavesdropping system that reconstructs intelligible speech solely from vibration signals induced by loudspeaker playback, even through walls and without prior knowledge of the speaker. To achieve this, we reveal an optimal combination of vibrating material and radar sampling rate for capturing high-quality vibrations using narrowband mmWave signals. We then design a deep neural network that reconstructs intelligible speech from the estimated noisy spectrograms. To further support downstream speech understanding, we introduce a synthetic training pipeline and selectively fine-tune the encoder of a pre-trained ASR model. We implement mmSpeech with a commercial mmWave radar and validate its performance through extensive experiments. Results show that mmSpeech achieves state-of-the-art speech quality and generalizes well across unseen speakers and various conditions.

cs.SD↗

arXiv2025

Active Domain Adaptation for mmWave-based HAR via Renyi Entropy-based Uncertainty Estimation

Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models with minimal labeled data. mmADA enhances adaptation by introducing Renyi Entropy-based uncertainty estimation to identify and label the most informative target samples. Additionally, it leverages contrastive learning and pseudo-labeling to refine feature alignment using unlabeled data. Evaluations with a TI IWR1443BOOST radar across multiple users, positions, and environments show that mmADA achieves over 90% accuracy in various cross-domain settings. Comparisons with five baselines confirm its superior adaptation performance, while further tests on unseen users, environments, and two additional open-source datasets validate its robustness and generalization.

cs.HC↗

arXiv2025

Building AI Literacy at Home: How Families Navigate Children's Self-Directed Learning with AI

As generative AI becomes embedded in children's learning spaces, families face new challenges in guiding its use. Middle childhood (ages 7-13) is a critical stage where children seek autonomy even as parental influence remains strong. Using self-directed learning (SDL) as a lens, we examine how parents perceive and support children's developing AI literacy through focus groups with 13 parent-child pairs. Parents described evolving phases of engagement driven by screen time, self-motivation, and growing knowledge. While many framed AI primarily as a study tool, few considered its non-educational roles or risks, such as privacy and infrastructural embedding. Parents also noted gaps in their own AI understanding, often turning to joint exploration and engagement as a form of co-learning. Our findings reveal how families co-construct children's AI literacy, exposing tensions between practical expectations and critical literacies, and provide design implications that foster SDL while balancing autonomy and oversight.

cs.HC↗

arXiv2025

"Learning Together": AI-Mediated Support for Parental Involvement in Everyday Learning

Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Although AI is increasingly present in learning environments, most systems remain child-centered and overlook the collaborative, distributed nature of family education. This paper investigates how AI can mediate family collaboration by addressing tensions of coordination, uneven workloads, and parental mediation. From a formative study with families using AI in daily learning, we identified challenges in responsibility sharing and recognition of contributions. Building on these insights, we designed FamLearn, an LLM-powered prototype that distributes tasks, visualizes contributions, and provides individualized support. A one-week field study with 11 families shows how this prototype can ease caregiving burdens, foster recognition, and enrich shared learning experiences. Our findings suggest that LLMs can move beyond the role of tutor to act as family mediators - balancing responsibilities, scaffolding intergenerational participation, and strengthening the relational fabric of family learning.

cs.HC↗

arXiv2025

Poisson Flow Consistency Training

The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++) which has achieved success in unconditional image generation and CT image denoising. Yet the PFCM can only be trained in distillation which limits the potential of the PFCM in many data modalities. The objective of this research was to create a method to train the PFCM in isolation called Poisson Flow Consistency Training (PFCT). The perturbation kernel was leveraged to remove the pretrained PFGM++, and the sinusoidal discretization schedule and Beta noise distribution were introduced in order to facilitate adaptability and improve sample quality. The model was applied to the task of low dose computed tomography image denoising and improved the low dose image in terms of LPIPS and SSIM. It also displayed similar denoising effectiveness as models like the Consistency Model. PFCT is established as a valid method of training the PFCM from its effectiveness in denoising CT images, showing potential with competitive results to other generative models. Further study is needed in the precise optimization of PFCT and in its applicability to other generative modeling tasks. The framework of PFCT creates more flexibility for the ways in which a PFCM can be created and can be applied to the field of generative modeling.

cs.CV↗

arXiv2025

Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging

X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value.

physics.med-ph↗

arXiv2025

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence

NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.

q-bio.NC↗

arXiv2025

Median2Median: Zero-shot Suppression of Structured Noise in Images

Image denoising is a fundamental problem in computer vision and medical imaging. However, real-world images are often degraded by structured noise with strong anisotropic correlations that existing methods struggle to remove. Most data-driven approaches rely on large datasets with high-quality labels and still suffer from limited generalizability, whereas existing zero-shot methods avoid this limitation but remain effective only for independent and identically distributed (i.i.d.) noise. To address this gap, we propose Median2Median (M2M), a zero-shot denoising framework designed for structured noise. M2M introduces a novel sampling strategy that generates pseudo-independent sub-image pairs from a single noisy input. This strategy leverages directional interpolation and generalized median filtering to adaptively exclude values distorted by structured artifacts. To further enlarge the effective sampling space and eliminate systematic bias, a randomized assignment strategy is employed, ensuring that the sampled sub-image pairs are suitable for Noise2Noise training. In our realistic simulation studies, M2M performs on par with state-of-the-art zero-shot methods under i.i.d. noise, while consistently outperforming them under correlated noise. These findings establish M2M as an efficient, data-free solution for structured noise suppression and mark the first step toward effective zero-shot denoising beyond the strict i.i.d. assumption.

eess.IV↗

arXiv2025

Development and Validation of a Large Language Model for Generating Fully-Structured Radiology Reports

Current LLMs for creating fully-structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage issues when uploading data to external servers.We aim to develop an open-source, accurate LLM for creating fully-structured and standardized LCS reports from varying free-text reports across institutions and demonstrate its utility in automatic statistical analysis and individual lung nodule retrieval. With IRB approvals, our retrospective study included 5,442 de-identified LDCT LCS radiology reports from two institutions. We constructed two evaluation datasets by labeling 500 pairs of free-text and fully-structured radiology reports and one large-scale consecutive dataset from January 2021 to December 2023. Two radiologists created a standardized template for recording 27 lung nodule features on LCS. We designed a dynamic-template-constrained decoding method to enhance existing LLMs for creating fully-structured reports from free-text radiology reports. Using consecutive structured reports, we automated descriptive statistical analyses and a nodule retrieval prototype. Our best LLM for creating fully-structured reports achieved high performance on cross-institutional datasets with an F1 score of about 97%, with neither formatting errors nor content hallucinations. Our method consistently improved the best open-source LLMs by up to 10.42%, and outperformed GPT-4o by 17.19%. The automatically derived statistical distributions were consistent with prior findings regarding attenuation, location, size, stability, and Lung-RADS. The retrieval system with structured reports allowed flexible nodule-level search and complex statistical analysis. Our developed software is publicly available for local deployment and further research.

cs.AI↗

arXiv2025

Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports

With the emergence of large-scale vision language models (VLM), it is now possible to produce realistic-looking radiology reports for chest X-ray images. However, their clinical translation has been hampered by the factual errors and hallucinations in the produced descriptions during inference. In this paper, we present a novel phrase-grounded fact-checking model (FC model) that detects errors in findings and their indicated locations in automatically generated chest radiology reports. Specifically, we simulate the errors in reports through a large synthetic dataset derived by perturbing findings and their locations in ground truth reports to form real and fake findings-location pairs with images. A new multi-label cross-modal contrastive regression network is then trained on this dataset. We present results demonstrating the robustness of our method in terms of accuracy of finding veracity prediction and localization on multiple X-ray datasets. We also show its effectiveness for error detection in reports of SOTA report generators on multiple datasets achieving a concordance correlation coefficient of 0.997 with ground truth-based verification, thus pointing to its utility during clinical inference in radiology workflows.

cs.CV↗

arXiv2025

Reasoning Language Model for Personalized Lung Cancer Screening

Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.

cs.LG↗

arXiv2025

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping

Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over 70% of cases. While pathology vision-language (VL) foundation models show promising zero-shot capabilities for common cancer subtyping, their clinical performance for rare cancers remains limited. Existing multi-instance learning (MIL) methods rely only on visual features, overlooking cross-modal knowledge and compromising interpretability critical for rare cancer diagnosis. To address this limitation, we propose PathPT, a novel framework that fully exploits the potential of vision-language pathology foundation models through spatially-aware visual aggregation and task-specific prompt tuning. Unlike conventional MIL, PathPT converts WSI-level supervision into fine-grained tile-level guidance by leveraging the zero-shot capabilities of VL models, thereby preserving localization on cancerous regions and enabling cross-modal reasoning through prompts aligned with histopathological semantics. We benchmark PathPT on eight rare cancer datasets(four adult and four pediatric) spanning 56 subtypes and 2,910 WSIs, as well as three common cancer datasets, evaluating four state-of-the-art VL models and four MIL frameworks under three few-shot settings. Results show that PathPT consistently delivers superior performance, achieving substantial gains in subtyping accuracy and cancerous region grounding ability. This work advances AI-assisted diagnosis for rare cancers, offering a scalable solution for improving subtyping accuracy in settings with limited access to specialized expertise.

cs.CV↗

arXiv2025

L3AC: Towards a Lightweight and Lossless Audio Codec

Neural audio codecs have recently gained traction for their ability to compress high-fidelity audio and provide discrete tokens for generative modeling. However, leading approaches often rely on resource-intensive models and complex multi-quantizer architectures, limiting their practicality in real-world applications. In this work, we introduce L3AC, a lightweight neural audio codec that addresses these challenges by leveraging a single quantizer and a highly efficient architecture. To enhance reconstruction fidelity while minimizing model complexity, L3AC explores streamlined convolutional networks and local Transformer modules, alongside TConv--a novel structure designed to capture acoustic variations across multiple temporal scales. Despite its compact design, extensive experiments across diverse datasets demonstrate that L3AC matches or exceeds the reconstruction quality of leading codecs while reducing computational overhead by an order of magnitude. The single-quantizer design further enhances its adaptability for downstream tasks. The source code is publicly available at https://github.com/zhai-lw/L3AC.

cs.SD↗

arXiv2025

LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT

Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and image domains, parameterized as composite functions of neural networks trained with domain-specific data. We allow these regularizers to be nonconvex and nonsmooth to extract features from data effectively. We minimize the overall objective function using Nesterov's smoothing technique and residual learning architecture. It is demonstrated that LAMA reduces network complexity, improves memory efficiency, and enhances reconstruction accuracy, stability, and interpretability. Extensive experiments show that LAMA significantly outperforms state-of-the-art methods on popular benchmark datasets for Computed Tomography.

cs.CV↗

arXiv2025

StyDeco: Unsupervised Style Transfer with Distilling Priors and Semantic Decoupling

Diffusion models have emerged as the dominant paradigm for style transfer, but their text-driven mechanism is hindered by a core limitation: it treats textual descriptions as uniform, monolithic guidance. This limitation overlooks the semantic gap between the non-spatial nature of textual descriptions and the spatially-aware attributes of visual style, often leading to the loss of semantic structure and fine-grained details during stylization. In this paper, we propose StyDeco, an unsupervised framework that resolves this limitation by learning text representations specifically tailored for the style transfer task. Our framework first employs Prior-Guided Data Distillation (PGD), a strategy designed to distill stylistic knowledge without human supervision. It leverages a powerful frozen generative model to automatically synthesize pseudo-paired data. Subsequently, we introduce Contrastive Semantic Decoupling (CSD), a task-specific objective that adapts a text encoder using domain-specific weights. CSD performs a two-class clustering in the semantic space, encouraging source and target representations to form distinct clusters. Extensive experiments on three classic benchmarks demonstrate that our framework outperforms several existing approaches in both stylistic fidelity and structural preservation, highlighting its effectiveness in style transfer with semantic preservation. In addition, our framework supports a unique de-stylization process, further demonstrating its extensibility. Our code is vailable at https://github.com/QuanjianSong/StyDeco.

cs.CV↗

arXiv2025

XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses

Human Action Recognition (HAR) plays a crucial role in applications such as health monitoring, smart home automation, and human-computer interaction. While HAR has been extensively studied, action summarization using Wi-Fi and IMU signals in smart-home environments , which involves identifying and summarizing continuous actions, remains an emerging task. This paper introduces the novel XRF V2 dataset, designed for indoor daily activity Temporal Action Localization (TAL) and action summarization. XRF V2 integrates multimodal data from Wi-Fi signals, IMU sensors (smartphones, smartwatches, headphones, and smart glasses), and synchronized video recordings, offering a diverse collection of indoor activities from 16 volunteers across three distinct environments. To tackle TAL and action summarization, we propose the XRFMamba neural network, which excels at capturing long-term dependencies in untrimmed sensory sequences and achieves the best performance with an average mAP of 78.74, outperforming the recent WiFiTAD by 5.49 points in mAP@avg while using 35% fewer parameters. In action summarization, we introduce a new metric, Response Meaning Consistency (RMC), to evaluate action summarization performance. And it achieves an average Response Meaning Consistency (mRMC) of 0.802. We envision XRF V2 as a valuable resource for advancing research in human action localization, action forecasting, pose estimation, multimodal foundation models pre-training, synthetic data generation, and more. The data and code are available at https://github.com/aiotgroup/XRFV2.

cs.CV↗

arXiv2025

LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models

Low-dose computed tomography (LDCT) reduces radiation exposure but often degrades image quality, potentially compromising diagnostic accuracy. Existing deep learning-based denoising methods focus primarily on pixel-level mappings, overlooking the potential benefits of high-level semantic guidance. Recent advances in vision-language models (VLMs) suggest that language can serve as a powerful tool for capturing structured semantic information, offering new opportunities to improve LDCT reconstruction. In this paper, we introduce LangMamba, a Language-driven Mamba framework for LDCT denoising that leverages VLM-derived representations to enhance supervision from normal-dose CT (NDCT). LangMamba follows a two-stage learning strategy. First, we pre-train a Language-guided AutoEncoder (LangAE) that leverages frozen VLMs to map NDCT images into a semantic space enriched with anatomical information. Second, we synergize LangAE with two key components to guide LDCT denoising: Semantic-Enhanced Efficient Denoiser (SEED), which enhances NDCT-relevant local semantic while capturing global features with efficient Mamba mechanism, and Language-engaged Dual-space Alignment (LangDA) Loss, which ensures that denoised images align with NDCT in both perceptual and semantic spaces. Extensive experiments on two public datasets demonstrate that LangMamba outperforms conventional state-of-the-art methods, significantly improving detail preservation and visual fidelity. Remarkably, LangAE exhibits strong generalizability to unseen datasets, thereby reducing training costs. Furthermore, LangDA loss improves explainability by integrating language-guided insights into image reconstruction and offers a plug-and-play fashion. Our findings shed new light on the potential of language as a supervisory signal to advance LDCT denoising. The code is publicly available on https://github.com/hao1635/LangMamba.

eess.IV↗